, Helium [3rd Floor]
AI-assisted coding became the default. Tools like GitHub Copilot, Cursor, and Claude can generate hundreds of lines of Python in seconds. However, the real challenge isn't how fast we generate code — it's how we ensure that generated code actually represents our intent, follows best practices, and integrates cleanly into existing systems.
In this talk, I present Spec-Driven Development (SDD), a way to engineer the context in which AI writes code. Using a realistic example from my work building production-grade retrieval-augmented generation systems, I show how specifications can become a practical way to interact with AI coding tools — grounded in a concrete use case, from spec to implementation.
AI Engineering is fundamentally about system building. It is the transition from demos to production-grade Python systems that must be scalable, reliable, and testable. In my experience, one way to achieve this consistently with AI-generated code is to stop coding first — and start specifying first.
Spec-Driven Development is a practical methodology for AI-assisted development. It is not about heavy bureaucracy; it's about creating a "Single Source of Truth" that both humans and AI agents can rely on.
In this talk, I will walk through a realistic feature in a production-grade retrieval-augmented generation system. I will demonstrate how I used SpecKit — one example of a structured spec workflow, usable with different AI coding assistants — to move from a feature request to a reviewable spec, a research document, interface contracts, and a phased task plan — all before writing a single line of implementation code.
What You Will Learn:
- What is Spec-Driven Development?
- The Paradigm Shift: Why "specifying" may be the new "coding" in a world of Large Language Models.
- How to use SpecKit as one example of a structured spec workflow — usable with different AI coding assistants.
Alina Dallmann is an AI Engineer at scieneers GmbH. As a computer scientist, she combines her passion for classical software engineering with modern, data-driven projects. Most recently, her focus has been on building production-ready Retrieval-Augmented Generation (RAG) systems.